([简体中文](./README_cn.md)|English) # Speech Verification) ## Introduction Speaker Verification, refers to the problem of getting a speaker embedding from an audio. This demo is an implementation to extract speaker embedding from a specific audio file. It can be done by a single command or a few lines in python using `PaddleSpeech`. ## Usage ### 1. Installation see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md). You can choose one way from easy, meduim and hard to install paddlespeech. ### 2. Prepare Input File The input of this demo should be a WAV file(`.wav`), and the sample rate must be the same as the model. Here are sample files for this demo that can be downloaded: ```bash wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav ``` ### 3. Usage - Command Line(Recommended) ```bash paddlespeech vector --task spk --input 85236145389.wav echo -e "demo1 85236145389.wav" > vec.job paddlespeech vector --task spk --input vec.job echo -e "demo2 85236145389.wav \n demo3 85236145389.wav" | paddlespeech vector --task spk ``` Usage: ```bash paddlespeech vector --help ``` Arguments: - `input`(required): Audio file to recognize. - `task` (required): Specify `vector` task. Default `spk`。 - `model`: Model type of vector task. Default: `ecapatdnn_voxceleb12`. - `sample_rate`: Sample rate of the model. Default: `16000`. - `config`: Config of vector task. Use pretrained model when it is None. Default: `None`. - `ckpt_path`: Model checkpoint. Use pretrained model when it is None. Default: `None`. - `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment. Output: ```bash demo [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 -9.723131 0.6619743 -6.976803 10.213478 7.494748 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 -3.539873 3.814236 5.1420674 2.162061 4.096431 -6.4162116 12.747448 1.9429878 -15.152943 6.417416 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 11.567354 3.69788 11.258265 7.442363 9.183411 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 -0.31784213 9.493548 2.1144536 4.358092 -12.089823 8.451689 -7.925461 4.6242585 4.4289427 18.692003 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 0.66607 15.443222 4.740594 -3.4725387 11.592567 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 4.532936 2.7264361 10.145339 -6.521951 2.897153 -3.3925855 5.079156 7.759716 4.677565 5.8457737 2.402413 7.7071047 3.9711342 -6.390043 6.1268735 -3.7760346 -11.118123 ] ``` - Python API ```python import paddle from paddlespeech.cli import VectorExecutor vector_executor = VectorExecutor() audio_emb = vector_executor( model='ecapatdnn_voxceleb12', sample_rate=16000, config=None, # Set `config` and `ckpt_path` to None to use pretrained model. ckpt_path=None, audio_file='./85236145389.wav', device=paddle.get_device()) print('Audio embedding Result: \n{}'.format(audio_emb)) ``` Output： ```bash # Vector Result: Audio embedding Result: [ 1.4217498 5.626253 -5.342073 1.1773866 3.308055 1.756596 5.167894 10.80636 -3.8226728 -5.6141334 2.623845 -0.8072968 1.9635103 -7.3128724 0.01103897 -9.723131 0.6619743 -6.976803 10.213478 7.494748 2.9105635 3.8949256 3.7999806 7.1061673 16.905321 -7.1493764 8.733103 3.4230042 -4.831653 -11.403367 11.232214 7.1274667 -4.2828417 2.452362 -5.130748 -18.177666 -2.6116815 -11.000337 -6.7314315 1.6564683 0.7618269 1.1253023 -2.083836 4.725744 -8.782597 -3.539873 3.814236 5.1420674 2.162061 4.096431 -6.4162116 12.747448 1.9429878 -15.152943 6.417416 16.097002 -9.716668 -1.9920526 -3.3649497 -1.871939 11.567354 3.69788 11.258265 7.442363 9.183411 4.5281515 -1.2417862 4.3959084 6.6727695 5.8898783 7.627124 -0.66919386 -11.889693 -9.208865 -7.4274073 -3.7776625 6.917234 -9.848748 -2.0944717 -5.135116 0.49563864 9.317534 -5.9141874 -1.8098574 -0.11738578 -7.169265 -1.0578263 -5.7216787 -5.1173844 16.137651 -4.473626 7.6624317 -0.55381083 9.631587 -6.4704556 -8.548508 4.3716145 -0.79702514 4.478997 -2.9758704 3.272176 2.8382776 5.134597 -9.190781 -0.5657382 -4.8745747 2.3165567 -5.984303 -2.1798875 0.35541576 -0.31784213 9.493548 2.1144536 4.358092 -12.089823 8.451689 -7.925461 4.6242585 4.4289427 18.692003 -2.6204622 -5.149185 -0.35821092 8.488551 4.981496 -9.32683 -2.2544234 6.6417594 1.2119585 10.977129 16.555033 3.3238444 9.551863 -1.6676947 -0.79539716 -8.605674 -0.47356385 2.6741948 -5.359179 -2.6673796 0.66607 15.443222 4.740594 -3.4725387 11.592567 -2.054497 1.7361217 -8.265324 -9.30447 5.4068313 -1.5180256 -7.746615 -6.089606 0.07112726 -0.34904733 -8.649895 -9.998958 -2.564841 -0.53999114 2.601808 -0.31927416 -1.8815292 -2.07215 -3.4105783 -8.2998085 1.483641 -15.365992 -8.288208 3.8847756 -3.4876456 7.3629923 0.4657332 3.132599 12.438889 -1.8337058 4.532936 2.7264361 10.145339 -6.521951 2.897153 -3.3925855 5.079156 7.759716 4.677565 5.8457737 2.402413 7.7071047 3.9711342 -6.390043 6.1268735 -3.7760346 -11.118123 ] ``` ### 4.Pretrained Models Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API: | Model | Sample Rate | :--- | :---: | | ecapatdnn_voxceleb12 | 16k